Results 21 to 30 of about 820,067 (303)
Alzheimer’s Research & Therapy, 2023 Background Developing a screening method for identifying individuals at higher risk of elevated brain amyloid burden is important to reduce costs and burden to patients in clinical trials on Alzheimer’s disease or the clinical setting.
Noriyuki Kimura +9 more
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Deep learning: Computational aspects [PDF]
WIREs Computational Statistics, 2020 AbstractIn this article, we review computational aspects of deep learning (DL). DL uses network architectures consisting of hierarchical layers of latent variables to construct predictors for high‐dimensional input–output models. Training a DL architecture is computationally intensive, and efficient linear algebra library is the key for training and ...
Nicholas Polson, Vadim Sokolov
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Unconstrained neighbor selection for minimum reconstruction error-based K-NN classifiers
Complex & Intelligent Systems, 2023 It is essential to define more convincing and applicable classifiers for small datasets. In this paper, a minimum reconstruction error-based K-nearest neighbors (K-NN) classifier is proposed. We propose a new neighbor selection method.
Rassoul Hajizadeh
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Deep learning in deep time [PDF]
Proceedings of the National Academy of Sciences, 2020 Digitized natural history records, now numbering in the billions (1), span widely across the tree of life and provide the foundation for numerous recent advances in biodiversity research (2, 3). Mechanistic insights are emerging for old questions, including how diversity has expanded and contracted through Earth’s history (4), how species have come to ...
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The Journal of Neuroscience, 2018 Animals successfully thrive in noisy environments with finite resources. The necessity to function with resource constraints has led evolution to design animal brains (and bodies) to be optimal in their use of computational power while being adaptable to their environmental niche.
Shyam Srinivasan +3 more
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Machine Learning, 2023 AbstractThis paper presents a novel holistic deep learning framework that simultaneously addresses the challenges of vulnerability to input perturbations, overparametrization, and performance instability from different train-validation splits. The proposed framework holistically improves accuracy, robustness, sparsity, and stability over standard deep ...
Dimitris Bertsimas +5 more
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Revista Brasileira de Ensino de Física, 2022 As a consequence of its capability of creating high level abstractions from data, deep learning has been effectively employed in a wide range of applications, including physics. Though deep learning can be, at first and simplistically understood in terms of very large neural networks, it also encompasses new concepts and methods. In order to understand
Arruda, Henrique F. de +3 more
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Epilepsia Open, 2023 Objective As of 2022, 36 anti‐seizure medications (ASMs) have been licensed for the treatment of epilepsy, however, adverse effects (AEs) are commonly reported.
Kazuyuki Fukushima +14 more
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Convolutional neural networks for heat conduction
Case Studies in Thermal Engineering, 2022 This paper presents a data-driven approach to solve heat conduction problems, in particular 2D heat conduction problems. The physical laws which govern such problems are modeled by partial differential equations.
Sidharth Tadeparti +1 more
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Applied Sciences, 2021 To properly restore masonry cultural heritage sites, the materials used for retrofitting can have a critical effect, and this requires standards for traditional Korean brick and lime mortar to be examined.
Gayoon Lee +4 more
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